Defect detection on glass-ware surfaces, particularly along crimped-crown-engaging surfaces is inherently problematic in high volume production applications. Detection is an essential quality assurance issue, since consumers view glass breakage as a material safety concern. Accordingly, it is important to reliably screen out all flawed bottles.
The high volume unit throughput adds to the difficulty of examining each and every bottle, and automated systems are essential for coping with the large numbers of bottles that have to be checked.
Moreover, even in automated systems, the setting of any kind of reasonably conservative detection thresholds with a view to identifying and screening out the preponderance of potential bottle failures seems to invariably lead to unacceptably high collateral rates of unnecessary rejection--that is bottles are identified as false positives for indicia of incipient breakage, when in fact they are not part of the "at risk" target group.
Discrimination between acceptable and unacceptable bottles is a two part process. The first step entails discriminatory sensing, and the second step entails discriminatory processing. Of the two, it is important to note that the efficacy and flexibility of the processing is contingent on the data quality that can be drawn from the sensing step.
The reliability of optical sensing for this purpose is frustrated by both external and internal reflections at bottle surfaces. This coupled with the fact that bottle surfaces are rarely flat, and may even be threaded or otherwise embossed, greatly exacerbates the problem of "noise" in defect "signal" acquisition. The "signal" must be sensitive enough to convey the qualitative and quantitative information necessary to discriminate surface damage from bottle threads.
Moreover, while highly sensitive optical sensing may be useful in applications for relatively pristine, single-trip, bottles onto which thin gauge crowns are rolled formed in-situ (as is common practice in the North American soft drink market, for example), the same may not be applicable to bottles which make multiple return trips, particularly where, as in the beer industry, the crowns are heavy gauge material that is crimped on under much more rigorous conditions than are ever employed in the aforementioned case. In the latter case, quantitative information relating the degree of damage is essential to effective discrimination.
Optical sensing systems are therefor broadly useful only to the extent that they can provide an information-rich signal that is readily and reliably discernable over any incidentally acquired noise, so that both the qualitative and quantitative aspects of that information are made available for use in the subsequent processing step. Accordingly a high "signal to noise" ratio is required if optical sensing is to be employed successfully in appropriately discriminating between bottles having damage that renders them prone to breakage, from those that are either not damaged or whose current-state of imperfection is not likely to engender bottle failure during at least the next succeeding trip.
One approach to optical sensing relies upon the use of a camera coupled with a computer. The camera is adapted to capture an image including details of the bottle thread, which it then passes to the computer. The computer matches the camera image with an overlapping "ideal" image that was previously stored in the computer, and then reacts to variations between the two images. This approach requires that the two images be mapped in a thread-matched overlay registration. Since the bottles in a high-speed, screening queue arrive at the camera viewing station with the threads presented in a random orientation, the computer must expend substantial computing resources to provide for the necessary image registration matching, even before it can effect the discriminatory processing step. This requires processing time and, as a practical matter, also requires substantial capital investment in raw computing power if the system is to cope with the large unit throughput of bottles that is encountered in contemporary industrial settings.
Accordingly, there remains a need in the art for high speed optical sensing that makes high signal-to-noise ratio information available for subsequent processing.